U.S. patent application number 14/849544 was filed with the patent office on 2016-03-10 for methods and systems for block least squares based non-linear interference management in multi-technology communication devices.
The applicant listed for this patent is QUALCOMM Incorporated. Invention is credited to Farrokh Abrishamkar, Brian Clarke Banister, Nicholas Michael Carbone, Insung Kang, Roberto Rimini, Sheng-Yuan TU.
Application Number | 20160072591 14/849544 |
Document ID | / |
Family ID | 55438531 |
Filed Date | 2016-03-10 |
United States Patent
Application |
20160072591 |
Kind Code |
A1 |
TU; Sheng-Yuan ; et
al. |
March 10, 2016 |
Methods and Systems for Block Least Squares Based Non-Linear
Interference Management in Multi-Technology Communication
Devices
Abstract
The various embodiments include methods and apparatuses for
canceling nonlinear interference during concurrent communication of
multi-technology wireless communication devices. Nonlinear
interference may be estimated using a block least squares function
interference filter by generating aggressor kernel matrices from
the aggressor signals, augmenting the aggressor kernel matrices by
weight factors and executing a linear combination of the augmented
output, at an intermediate layer to produce intermediate layer
outputs. At an output layer, a linear filter function may be
executed on the intermediate layer outputs to produce an estimated
nonlinear interference used to cancel the nonlinear interference of
a victim signal.
Inventors: |
TU; Sheng-Yuan; (San Diego,
CA) ; Abrishamkar; Farrokh; (San Diego, CA) ;
Banister; Brian Clarke; (San Diego, CA) ; Rimini;
Roberto; (San Diego, CA) ; Carbone; Nicholas
Michael; (San Diego, CA) ; Kang; Insung; (San
Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
QUALCOMM Incorporated |
San Diego |
CA |
US |
|
|
Family ID: |
55438531 |
Appl. No.: |
14/849544 |
Filed: |
September 9, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62048519 |
Sep 10, 2014 |
|
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62106759 |
Jan 23, 2015 |
|
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62106764 |
Jan 23, 2015 |
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Current U.S.
Class: |
455/63.1 |
Current CPC
Class: |
H04B 1/0475 20130101;
H04W 88/06 20130101; H04B 1/525 20130101 |
International
Class: |
H04B 15/00 20060101
H04B015/00 |
Claims
1. A method for managing signal interference in a multi-technology
communication device, comprising: receiving an aggressor signal at
an input layer of a filtering construct; generating a real
aggressor kernel matrix, an imaginary aggressor kernel matrix, and
a combined aggressor kernel matrix from the aggressor signal;
augmenting the real aggressor kernel matrix and the imaginary
aggressor kernel matrix with weight factors at an intermediate
layer of the filtering construct; linearly combining the augmented
real aggressor kernel matrix at the intermediate layer to produce
real intermediate layer outputs, and the augmented imaginary
aggressor kernel matrix at the intermediate layer to produce
imaginary intermediate layer outputs; and executing a linear filter
function on the real intermediate layer outputs and the imaginary
intermediate layer outputs at an output layer of the filtering
construct to obtain estimated nonlinear interference.
2. The method of claim 1, further comprising: determining an error
of the estimated nonlinear interference; determining whether the
error of the estimated nonlinear interference exceeds an efficiency
threshold; and canceling the estimated nonlinear interference from
a victim signal.
3. The method of claim 2, further comprising training the weight
factors to reduce the error of the estimated nonlinear
interference.
4. The method of claim 3, wherein: training the weight factors to
reduce the error of the estimated nonlinear interference comprises
training weight factors in response to determining that the error
of the estimated nonlinear interference exceeds the efficiency
threshold, and canceling the estimated nonlinear interference from
the victim signal comprises canceling the estimated nonlinear
interference from the victim signal in response to determining that
the error of the estimated nonlinear interference does not exceed
the efficiency threshold.
5. The method of claim 3, further comprising training the weight
factors using a least squares method.
6. The method of claim 1, further comprising estimating an initial
value of the weight factors using the combined aggressor kernel
matrix.
7. The method of claim 1, wherein the linear filter function is a
finite impulse response filter.
8. The method of claim 1, wherein the linear filter function has a
Hammerstein structure.
9. The method of claim 1, wherein the received aggressor signal
represents the aggressor signal received by an antenna of the
multi-technology communication device at a specific instance in
time.
10. The method of claim 1, wherein generating the real aggressor
kernel matrix and the imaginary aggressor kernel matrix comprises:
separating the aggressor signal into a real aggressor component and
an imaginary aggressor component; executing a kernel function on
the real aggressor component and the imaginary aggressor component
to obtain an aggressor kernel associated with an order of the
kernel function, and having a real kernel component and an
imaginary kernel component.
11. The method of claim 10, further comprising: continuing to
execute the kernel function from the order 1 to "p"; inserting the
real kernel component associated with the order from 1 to "p" into
the real aggressor kernel matrix; inserting the imaginary kernel
component associated with the order from 1 to "p" to the imaginary
aggressor kernel matrix; and inserting the real aggressor kernel
matrix and the imaginary aggressor kernel matrix into the combined
aggressor kernel matrix.
12. The method of claim 11, wherein "p" equals 7.
13. The method of claim 11, wherein each instance of the order is
an odd number.
14. The method of claim 1, wherein each of the real aggressor
kernel matrix and the imaginary aggressor kernel matrix is a set of
non-linear inputs derived from the aggressor signal.
15. The method of claim 1, further comprising canceling the
estimated nonlinear interference from a victim signal received by
an antenna.
16. The method of claim 15, further comprising decoding the victim
signal after canceling the estimated nonlinear interference from
the victim signal.
17. The method of claim 1, further comprising training a second set
of weight factors associated with the linear filter function using
a matrix including the real intermediate layer outputs and the
imaginary intermediate layer outputs.
18. The method of claim 17, wherein the second set of weight
factors is trained using a least squares method.
19. A multi-technology communication device, comprising: an
antenna; a processor communicatively connected to the antenna and
configured with processor-executable instructions to perform
operations comprising: receiving an aggressor signal at an input
layer of a filtering construct; generating a real aggressor kernel
matrix, an imaginary aggressor kernel matrix, and a combined
aggressor kernel matrix from the aggressor signal; augmenting the
real aggressor kernel matrix and the imaginary aggressor kernel
matrix with weight factors at an intermediate layer of the
filtering construct; linearly combining the augmented real
aggressor kernel matrix at the intermediate layer to produce real
intermediate layer outputs, and the augmented imaginary aggressor
kernel matrix at the intermediate layer to produce imaginary
intermediate layer outputs; and executing a linear filter function
on the real intermediate layer outputs and the imaginary
intermediate layer outputs at an output layer of the filtering
construct to obtain estimated nonlinear interference.
20. The multi-technology communication device of claim 19, wherein
the processor is configured with processor-executable instructions
to perform operations further comprising estimating an initial
value of the weight factors using the combined aggressor kernel
matrix.
21. The multi-technology communication device of claim 19, wherein
the received aggressor signal represents the aggressor signal
received by the antenna of the multi-technology communication
device at a specific instance in time.
22. The multi-technology communication device of claim 19, wherein
the processor is configured with processor-executable instructions
such that generating the real aggressor kernel matrix and the
imaginary aggressor kernel matrix comprises: separating the
aggressor signal into a real aggressor component and an imaginary
aggressor component; and executing a kernel function on the real
aggressor component and the imaginary aggressor component to obtain
an aggressor kernel associated with an order of the kernel
function, and having a real kernel component and an imaginary
kernel component.
23. The multi-technology communication device of claim 22, wherein
the processor is configured with processor-executable instructions
to perform operations further comprising: continuing to execute the
kernel function from the order 1 to "p"; inserting the real kernel
component associated with the order from 1 to "p" into the real
aggressor kernel matrix; inserting the imaginary kernel component
associated with the order from 1 to "p" to the imaginary aggressor
kernel matrix; and inserting the real aggressor kernel matrix and
the imaginary aggressor kernel matrix into the combined aggressor
kernel matrix.
24. The multi-technology communication device of claim 23, wherein
"p" equals 7.
25. The multi-technology communication device of claim 19, wherein
the processor is configured with processor-executable instructions
such that each of the real aggressor kernel matrix and the
imaginary aggressor kernel matrix is a set of non-linear inputs
derived from the aggressor signal.
26. The multi-technology communication device of claim 19, wherein
the processor is configured with processor-executable instructions
to perform operations further comprising canceling the estimated
nonlinear interference from a victim signal received by an
antenna.
27. The multi-technology communication device of claim 26, wherein
the processor is configured with processor-executable instructions
to perform operations further comprising decoding the victim signal
after canceling the estimated nonlinear interference from the
victim signal.
28. The multi-technology communication device of claim 19, wherein
the processor is configured with processor-executable instructions
to perform operations further comprising training a second set of
weight factors associated with the linear filter function using a
matrix including the real intermediate layer outputs and the
imaginary intermediate layer outputs.
29. A multi-technology communication device, comprising: means for
receiving an aggressor signal at an input layer of a filtering
construct; means for generating a real aggressor kernel matrix, an
imaginary aggressor kernel matrix, and a combined aggressor kernel
matrix from the aggressor signal; means for augmenting the real
aggressor kernel matrix and the imaginary aggressor kernel matrix
with weight factors at an intermediate layer of the filtering
construct; means for linearly combining the augmented real
aggressor kernel matrix at the intermediate layer to produce real
intermediate layer outputs, and the augmented imaginary aggressor
kernel matrix at the intermediate layer to produce imaginary
intermediate layer outputs; and means for executing a linear filter
function on the real intermediate layer outputs and the imaginary
intermediate layer outputs at an output layer of the filtering
construct to obtain estimated nonlinear interference.
30. A non-transitory processor-readable medium having stored
thereon processor-executable software instructions to cause a
processor of a multi-technology communication device to perform
operations comprising: receiving an aggressor signal at an input
layer of a filtering construct; generating a real aggressor kernel
matrix, an imaginary aggressor kernel matrix, and a combined
aggressor kernel matrix from the aggressor signal; augmenting the
real aggressor kernel matrix and the imaginary aggressor kernel
matrix with weight factors at an intermediate layer of the
filtering construct; linearly combining the augmented real
aggressor kernel matrix at the intermediate layer to produce real
intermediate layer outputs, and the augmented imaginary aggressor
kernel matrix at the intermediate layer to produce imaginary
intermediate layer outputs; and executing a linear filter function
on the real intermediate layer outputs and the imaginary
intermediate layer outputs at an output layer of the filtering
construct to obtain estimated nonlinear interference.
Description
RELATED APPLICATIONS
[0001] This application claims the benefit of priority under 35
U.S.C. .sctn.119(e) of U.S. Provisional Application No. 62/048,519
entitled "Multilayer Perceptron For Dual SIM Dual Active
Interference Cancellation" filed Sep. 10, 2014, associated U.S.
Non-provisional Patent Application No. 62/106,759 entitled "Block
Least Squares Interference Filter for Dual SIM Dual Active
Interference Cancellation" filed Jan. 23, 2015, and U.S.
Provisional Application No. 62/106,764 entitled "Multi-Model Block
Least Squares/Radial Basis Function Neural Network for Dual SIM
Dual Active Interference Cancellation" filed Jan. 23, 2015, the
entire contents of all of which are hereby incorporated by
reference.
BACKGROUND
[0002] Some wireless communication devices--such as smart phones,
tablet computers, laptop computers, and routers--contain hardware
and/or software elements that provide access to multiple wireless
communication networks simultaneously. For example, a wireless
communication device can have one or more radio frequency
communication circuits (or "RF chains") for accessing one or more
wireless local area networks ("WLANs"), wireless wide area networks
("WWANs"), and/or global positioning systems ("GPS"). When multiple
reception ("Rx") and/or transmission ("Tx") operations are
implemented simultaneously, i.e., co-exist, on a wireless
communication device, these operations may interfere with each
other.
SUMMARY
[0003] The methods and apparatuses of various embodiments provide
circuits and methods for managing interference in a
multi-technology communication device. Embodiment methods may
include receiving an aggressor signal at an input layer of a
filtering construct, generating a real aggressor kernel matrix, an
imaginary aggressor kernel matrix, and a combined aggressor kernel
matrix from the aggressor signal, augmenting the real aggressor
kernel matrix and the imaginary aggressor kernel matrix with weight
factors at an intermediate layer of the filtering construct,
linearly combining the augmented real aggressor kernel matrix at
the intermediate layer to produce real intermediate layer outputs
and the augmented imaginary aggressor kernel matrix at the
intermediate layer to produce imaginary intermediate layer outputs,
and executing a linear filter function on the real intermediate
layer outputs and the imaginary intermediate layer outputs at an
output layer of the filtering construct to obtain estimated
nonlinear interference.
[0004] Some embodiments may further include determining an error of
the estimated nonlinear interference, determining whether the error
of the estimated nonlinear interference exceeds an efficiency
threshold, and canceling the estimated nonlinear interference from
a victim signal. Such embodiments may further include training the
weight factors to reduce an error of the estimated nonlinear
interference. In such embodiments, training the weight factors to
reduce an error of the estimated nonlinear interference may include
training weight factors in response to determining that the error
of the estimated nonlinear interference exceeds the efficiency
threshold. In such embodiments, canceling the estimated nonlinear
interference from the victim signal may include canceling the
estimated nonlinear interference from the victim signal in response
to determining that the error of the estimated nonlinear
interference does not exceed the efficiency threshold. Some
embodiments may include training the weight factors using a least
squares method.
[0005] Some embodiments may further include estimating an initial
value of the weight factors using the combined aggressor kernel
matrix.
[0006] In some embodiments, the linear filter function may be a
finite impulse response filter. In some embodiments, the linear
filter function may have a Hammerstein structure.
[0007] In some embodiments, the received aggressor signal may
represent an aggressor signal received by an antenna of the
multi-technology communication device at a specific instance in
time.
[0008] In some embodiments, generating an aggressor kernel matrix
may include separating the aggressor signal into a real value
component and an imaginary value component, and executing a kernel
function of a pre-determined order on the real value component and
imaginary value component to obtain an aggressor kernel associated
with the pre-determined order and having a real value kernel
component and an imaginary value kernel component. Such embodiments
may further include continuing the operation of executing the
kernel function of pre-determined order from order 1 to order p,
inserting the real value components of the aggressor kernel
associated with each pre-determined order from 1 to p to a real
aggressor kernel matrix, inserting the imaginary value components
of the aggressor kernel associated with each pre-determined order
from 1 to p to an imaginary aggressor kernel matrix, and inserting
the real aggressor kernel matrix and the imaginary aggressor kernel
matrix into a combined aggressor kernel matrix. In such
embodiments, p may equal 7. In some embodiments, each of the
pre-determined orders may be an odd number.
[0009] In some embodiments, each of the real aggressor kernel
matrix and imaginary aggressor kernel matrix may be a set of
non-linear inputs derived from the received aggressor signal.
[0010] Some embodiments may further include canceling the estimated
nonlinear interference from a victim signal received by an antenna.
Such embodiments may further include, decoding the victim signal
after canceling the estimated nonlinear interference from the
victim signal.
[0011] Some embodiments may further include training a second set
of weight factors associated with the linear filter function using
a matrix including the real intermediate layer outputs and
imaginary intermediate layer outputs. In such embodiments, the
second set of weights may be trained using a least squares
method.
[0012] Embodiments include a multi-technology communication device
having an antenna and a processor communicatively connected to the
antenna and configured with processor-executable instructions to
perform operations of one or more of the embodiment methods
described above.
[0013] Embodiments include a multi-technology communication device
having means for performing functions of one or more of the
embodiment methods described above.
[0014] Embodiments include a non-transitory processor-readable
medium having stored thereon processor-executable software
instructions configured to cause a processor of a multi-technology
communication device to perform operations of one or more of the
embodiment methods described above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] The accompanying drawings, which are incorporated herein and
constitute part of this specification, illustrate exemplary
embodiments of the claims, and together with the general
description given above and the detailed description given below,
serve to explain the features of the claims.
[0016] FIG. 1 is a communication system block diagram illustrating
a network suitable for use with various embodiments.
[0017] FIG. 2 is a component block diagram illustrating various
embodiments of a multi-technology wireless communications
device.
[0018] FIG. 3 is a component block diagram illustrating an
interaction between components of different transmit/receive chains
in various embodiments of a multi-technology wireless
communications device.
[0019] FIG. 4 is a component block diagram illustrating a block
least squares interference filter for interference cancellation in
accordance with various embodiments.
[0020] FIG. 5 is a component block diagram illustrating layers of a
block least squares interference filter for interference
cancellation in accordance with various embodiments.
[0021] FIGS. 6A-B are functional block diagrams illustrating
interaction between components of a block least squares
interference filter for interference cancellation in accordance
with various embodiments.
[0022] FIG. 7 is a process flow diagram illustrating a method for
canceling nonlinear interference using a block least squares
interference filter in various embodiments of a multi-technology
wireless communications device in accordance with various
embodiments.
[0023] FIG. 8 is a process flow diagram illustrating a method for
estimating nonlinear interference using a block least squares
interference filter in a multi-technology wireless communications
device in accordance with various embodiments.
[0024] FIG. 9 is a process flow diagram illustrating a method for
training weight factors for use in a block least squares
interference filter in a multi-technology wireless communications
device in accordance with various embodiments.
[0025] FIG. 10 is a component diagram of an example
multi-technology wireless communication device suitable for use
with various embodiments.
DETAILED DESCRIPTION
[0026] The various embodiments will be described in detail with
reference to the accompanying drawings. Wherever possible, the same
reference numbers will be used throughout the drawings to refer to
the same or like parts. References made to particular examples and
implementations are for illustrative purposes, and are not intended
to limit the scope of the claims or the claims.
[0027] The word "exemplary" is used herein to mean "serving as an
example, instance, or illustration." Any implementation described
herein as "exemplary" is not necessarily to be construed as
preferred or advantageous over other implementations.
[0028] The terms "computing device," "mobile device," and "wireless
communication device" are used interchangeably herein to refer to
any one or all of cellular telephones, smartphones, personal or
mobile multi-media players, personal data assistants (PDAs), laptop
computers, tablet computers, smartbooks, ultrabooks, palm-top
computers, wireless electronic mail receivers, multimedia Internet
enabled cellular telephones, wireless gaming controllers, and
similar personal electronic devices which include a memory, a
programmable processor and wireless communication circuitry. As
used herein, the terms "multi-technology communication device" and
"multi-technology communication device" refer to a wireless
communication device that supports access to at least two mobile
communication networks. While the various embodiments are
particularly useful for mobile devices, such as smartphones, the
embodiments are generally useful in any electronic device that
implements radio hardware in close proximity to other hardware.
[0029] Descriptions of the various embodiments refer to
multi-technology communication devices for exemplary purposes.
However, the embodiments may be suitable for any
multiple-technology (multi-technology) wireless communication
device that may individually maintain a plurality of connections to
a plurality of mobile networks through one or more radio
communication circuits. For example, the various embodiments may be
implemented in multi-SIM multi-active devices of any combination of
number of subscriber identity modules (SIM) and concurrently active
subscriptions. Moreover, a SIM may not be required for a wireless
communication device to implement the various embodiments, which
may apply to any form of wireless communication.
[0030] In a wireless communication device with multiple RF chains,
the antennas of the RF chains may be in close proximity to each
other. This close proximity may cause one RF chain to desensitize
or interfere with the ability of another during the simultaneous
use of the RF chains. Receiver desensitization ("desense"), or
degradation of receiver sensitivity, may result from noise
interference of a nearby transmitter. In particular, when two
radios are close together with one transmitting on the uplink (the
"aggressor") and the other receiving on the downlink (the
"victim"), signals from the transmitter may be picked up by the
receiver or otherwise interfere with reception of a weaker signal
(e.g., from a distant base station). As a result, the received
signals may become corrupted and difficult or impossible for the
victim to decode. In particular, desense of received signals
presents a design and operational challenge for multi-radio devices
due to the proximity of transmitter and receiver.
[0031] Multi-technology devices enable a user to connect to
different mobile networks (or different accounts on the same
network) while using the same multi-technology communication
device. For example, a multi-technology communication device may
connect to GSM, TDSCDMA, CDMA2000, WCDMA and other radio frequency
networks. In the various embodiments, multi-technology
communication devices may also include two RF chains so that each
network communication supported by each RF chain can be
accomplished concurrently.
[0032] However, multi-technology devices can suffer from
interference between two communications being accomplished
concurrently, such as when one communication session is
transmitting ("Tx") at the same time as another communication
session is attempting to receive ("Rx"). As used herein, the term
"victim" refers to the communication service or subscription
suffering from interference at a given instant, and the term
"aggressor" refers to the communication service or subscription
whose Rx or Tx actions are causing the interference. In an example
multi-technology communication device, the victim may be attempting
to receive RF signals from a network while the aggressor attempts
to transmit RF signals to another network. In an example of such
interference, an aggressor's transmissions may de-sense the
victim's reception, in which case the victim may receive the
aggressor's transmissions that act as noise and interfere with the
victim's ability to receive wanted RF signals.
[0033] In multi-technology communication devices, an aggressor's
transmissions may cause severe impairment to the victim's ability
to receive transmission. This interference may be in the form of
blocking interference, harmonics, intermodulation, or other noises
and distortion. Such interference may significantly degrade the
victim's receiver sensitivity, link to a network, voice call
quality and data throughput. These effects may result in a reduced
network capacity for the affected communication service or
subscription. The aggressor's transmission may also cause receiver
sensitivity of the victim signal that is drastically degraded,
resulting in call quality degradation, higher rates for call drops
and radio link failures, and data throughput degradation, which may
potentially cause the victim to lose a data connection.
[0034] Nonlinear signals of the RF chains may be to blame for
desense of received signals. Often the Tx/aggressor signal
frequency is a fraction of the Rx/victim signal frequency. However,
multiple aggressor signals may constructively combine to form a
harmonic aggressor signal to the victim signal. The harmonic
aggressor signal may be strong enough to cause nonlinear
interference of the victim signal.
[0035] In order to recover information from the victim signal,
various circuits and processing methods may be used to remove or
subtract the interfering signals from the received victim signal.
However, removing or subtracting nonlinear interference from a
victim signal is particularly problematic for devices having
multiple RF chain, such as multi-SIM multi-active ("MSMA") devices
and for Long-Term Evolution ("LTE") carrier aggregation, because
interference experienced on one RF chain may come from multiple RF
sources and thus may have unpredictable signal form. Current
techniques for removing nonlinear interference from a victim signal
are case specific, requiring the communications device to have
knowledge of the communication technology used for the transmission
and reception of signals, and the kind of interference the
aggressor signal is causing.
[0036] The various embodiments include methods for removing
nonlinear interference from a victim signal in digital
communications by using an interference filter analysis method to
estimate the coefficients of the signal to be removed before a
received signal is decoded. In particular, the interference filter
may implement supervised learning using block least squares methods
with a linear filter to dynamically estimate an interference of the
aggressor signals on the victim signal to be removed from the
victim signal so that it may be decoded. An absolute calculation of
the nonlinear interference may be mathematically difficult.
Accordingly, the various embodiments provide methods that may be
implemented in cost effective circuits and processing algorithms to
provide an effective estimate of the interference, which when
subtracted from the victim signal results in significant
improvement in the recovered signal.
[0037] In various embodiments, a mobile device may use the block
least squares interference filter method combined with a linear
filter function to estimate a function of the nonlinear
interference from a set of known aggressor reference signals and a
victim reference signal without having to know the type of
communication technology or type, source or form of interference.
The set of aggressor reference signals may be obtained from the RF
chain on the mobile device supporting the aggressor reference
signals. The victim reference signal may be obtained from the RF
chain on the mobile device supporting the victim reference signal.
These known signals may be received by the block least squares
interference filter at an input layer. In various embodiments, the
aggressor reference signal may be a complex signal that may be
divided into one or more real and imaginary aggressor signals. In
various embodiments, the aggressor reference signal may be used to
generate a dominant kernel of aggressor signals, which may be
divided into one or more real and imaginary aggressor kernels and
added to aggressor kernel matrices. The aggressor kernel matrices
may be received by the block least squares interference filter at
the input layer in place of the aggressor reference signal. From
the input layer the block least squares interference filter may
pass the aggressor kernel matrices to an intermediate layer of the
block least squares interference filter. In passing these matrices
to nodes of the intermediate layer, the aggressor kernel matrices
may be combined and augmented using weight factors. The augmented
aggressor kernel matrices may be linearly combined. An output of
the intermediate layer may be passed to an output layer. All of the
outputs of the intermediate layer may again be augmented by a
second set of weight factors and linearly combined. An output of
the output layer may be an estimated nonlinear interference which
represents an estimation of an aggressor signal distorting the
victim signal. The estimated nonlinear interference may then be
removed from a received victim signal.
[0038] The various embodiments may be implemented in wireless
communication devices that operate within a variety of
communication systems 100, such as at least two mobile telephony
networks, an example of which is illustrated in FIG. 1. A first
mobile network 102 and a second mobile network 104 are typical
mobile networks that include a plurality of cellular base stations
130 and 140. A first multi-technology communication device 110 may
be in communication with the first mobile network 102 through a
cellular connection 132 to a first base station 130. The first
multi-technology communication device 110 may also be in
communication with the second mobile network 104 through a cellular
connection 142 to a second base station 140. The first base station
130 may be in communication with the first mobile network 102 over
a connection 134. The second base station 140 may be in
communication with the second mobile network 104 over a connection
144.
[0039] A second multi-technology communication device 120 may
similarly communicate with the first mobile network 102 through a
radio based communication connection such as a cellular connection
132 to a first base station 130. The second multi-technology
communication device 120 may communicate with the second mobile
network 104 through a radio communication connection such as a
cellular connection 142 to the second base station 140. Cellular
connections 132 and 142 may be made through two-way wireless
communication links, such as 4G, 3G, CDMA, TDSCDMA, WCDMA, GSM, and
other mobile telephony communication technologies. Other radio
communication connections may include various other wireless
connections, including WLANs, such as Wi-Fi based on IEEE 802.11
standards, and wireless location services, such as GPS. For
example, the first wireless communications device may transmit and
receive WiFi communications from a network resource such as a
router. Similarly, the wireless communications device may transmit
and receive wireless communications with multiple Bluetooth enabled
devices such as peripheral devices (e.g., keyboards, speakers,
displays) as well as the second wireless communications device. The
transmission and receipt of wireless communications over any and
all of these radio resources may result in desense on victim
signals during overlapping periods of transmission.
[0040] FIG. 2 illustrates various embodiments of a multi-technology
communication device 200 (e.g., 110, 120 in FIG. 1) that are
suitable for implementing the various embodiments. With reference
to FIGS. 1 and 2, the multi-technology communication device 200 may
include a first SIM interface 202a, which may receive a first
identity module SIM-1 204a that is associated with a first
subscription. The multi-technology communication device 200 may
also include a second SIM interface 202b, which may receive a
second identity module SIM-2 204b that is associated with a second
subscription.
[0041] A SIM in the various embodiments may be a Universal
Integrated Circuit Card (UICC) that is configured with SIM and/or
USIM applications, enabling access to, for example, GSM and/or UMTS
networks. The UICC may also provide storage for a phone book and
other applications. Alternatively, in a CDMA network, a SIM may be
a UICC removable user identity module (R-UIM) or a CDMA subscriber
identity module (CSIM) on a card.
[0042] Each SIM may have a CPU, ROM, RAM, EEPROM and I/O circuits.
A SIM used in the various embodiments may contain user account
information, an international mobile subscriber identity (IMSI), a
set of SIM application toolkit (SAT) commands and storage space for
phone book contacts. A SIM may further store a Home
Public-Land-Mobile-Network (HPLMN) code to indicate the SIM card
network operator provider. An Integrated Circuit Card Identity
(ICCID) SIM serial number may be printed on the SIM for
identification.
[0043] Each multi-technology communication device 200 may include
at least one controller, such as a general purpose processor 206,
which may be coupled to a coder/decoder (CODEC) 208. The CODEC 208
may in turn be coupled to a speaker 210 and a microphone 212. The
general purpose processor 206 may also be coupled to at least one
memory 214. The memory 214 may be a non-transitory tangible
computer readable storage medium that stores processor-executable
instructions. For example, the instructions may include routing
communication data relating to the first or second subscription
though a corresponding baseband-RF resource chain.
[0044] The memory 214 may store operating system (OS), as well as
user application software and executable instructions. The memory
214 may also store application data, such as an array data
structure.
[0045] The general purpose processor 206 and memory 214 may each be
coupled to at least one baseband modem processor 216. Each SIM in
the multi-technology communication device 200 (e.g., SIM-1 202a and
SIM-2 202b) may be associated with a baseband-RF resource chain.
Each baseband-RF resource chain may include the baseband modem
processor 216 to perform baseband/modem functions for
communications on a SIM, and one or more amplifiers and radios,
referred to generally herein as RF resources 218a, 218b. In some
embodiments, baseband-RF resource chains may interact with a shared
baseband modem processor 216 (i.e., a single device that performs
baseband/modem functions for all SIMs on the wireless device).
Alternatively, each baseband-RF resource chain may include
physically or logically separate baseband processors (e.g., BB1,
BB2).
[0046] In some embodiments, the baseband modem processor 216 may be
an integrated chip capable of managing the protocol stacks of each
of the SIMs or subscriptions (e.g., PS1, PS1) and implementing a
co-existence manager software 228 (e.g., CXM). By implementing
modem software, subscription protocol stacks, and the co-existence
manager software 228 on this integrated baseband modem processor
216, thread based instructions may be used on the integrated
baseband modem processor 216 to communicate instructions between
the software implementing the interference prediction, the
mitigation techniques for co-existence issues, and the Rx and Tx
operations.
[0047] The RF resources 218a, 218b may be communication circuits or
transceivers that perform transmit/receive functions for the
associated SIM of the wireless device. The RF resources 218a, 218b
may be communication circuits that include separate transmit and
receive circuitry, or may include a transceiver that combines
transmitter and receiver functions. The RF resources 218a, 218b may
be coupled to a wireless antenna (e.g., a first wireless antenna
220a and a second wireless antenna 220b). The RF resources 218a,
218b may also be coupled to the baseband modem processor 216.
[0048] In some embodiments, the general purpose processor 206,
memory 214, baseband processor(s) 216, and RF resources 218a, 218b
may be included in the multi-technology communication device 200 as
a system-on-chip. In other embodiments, the first and second SIMs
202a, 202b and their corresponding interfaces 204a, 204b may be
external to the system-on-chip. Further, various input and output
devices may be coupled to components on the system-on-chip, such as
interfaces or controllers. Example user input components suitable
for use in the multi-technology communication device 200 may
include, but are not limited to, a keypad 224 and a touchscreen
display 226.
[0049] In some embodiments, the keypad 224, touchscreen display
226, microphone 212, or a combination thereof, may perform the
function of receiving the request to initiate an outgoing call. For
example, the touchscreen display 226 may receive a selection of a
contact from a contact list or receive a telephone number. In
another example, either or both of the touchscreen display 226 and
microphone 212 may perform the function of receiving a request to
initiate an outgoing call. For example, the touchscreen display 226
may receive a selection of a contact from a contact list or receive
a telephone number. As another example, the request to initiate the
outgoing call may be in the form of a voice command received via
the microphone 212. Interfaces may be provided between the various
software modules and functions in multi-technology communication
device 200 to enable communication between them, as is known in the
art.
[0050] In some embodiments, the multi-technology communication
device 200 may instead be a single-technology or
multiple-technology device having more or less than two RF chains.
Further, various embodiments may implement, single RF chain or
multiple RF chain wireless communication devices with fewer SIM
cards than the number of RF chains, including without using any SIM
card.
[0051] FIG. 3 is a block diagram of a communication system 300 that
illustrates embodiment interactions between components of different
transmit/receive chains in a multi-technology wireless
communications device. With reference to FIGS. 1-3, for example, a
first radio technology RF chain 302 may be one RF resource 218a,
and a second radio technology RF chain 304 may be part of another
RF resource 218b. In some embodiments, the first and second radio
technology RF chains 302, 304 may include components operable for
transmitting data. When transmitting data, a data processor 306,
320 may format, encode, and interleave data in preparation for
transmission. A modulator/demodulator 308, 318 may modulate a
carrier signal with encoded data, for example, by performing
Gaussian minimum shift keying (GMSK). One or more transceiver
circuits 310, 316 may condition the modulated signal (e.g., by
filtering, amplifying, and up-converting) to generate a RF
modulated signal for transmission. The RF modulated signal may be
transmitted, for example, to the base station 130, 140 via an
antenna, such as the antenna 220a, 220b.
[0052] The components of the first and second radio technology RF
chains 302, 304 may also be operable to receive data. When
receiving data, the antenna 220a, 220b may receive RF modulated
signals from the base station 130, 140 for example. The one or more
transceiver circuits 310, 316 may condition (e.g., filter, amplify,
and down-convert) the received RF modulated signal, digitize the
conditioned signal, and provide samples to the
modulator/demodulator 308, 318. The modulator/demodulator 308, 318
may extract the original information-bearing signal from the
modulated carrier wave, and may provide the demodulated signal to
the data processor 306, 320. The data processor 306, 320 may
de-interleave and decode the signal to obtain the original, decoded
data, and may provide decoded data to other components in the
wireless device.
[0053] Operations of the first and second radio technology RF
chains 302, 304 may be controlled by a processor, such as the
baseband processor(s) 216. In the various embodiments, each of the
first and second radio technology RF chains 302, 304 may be
implemented as circuitry that may be separated into respective
receive and transmit circuits (not shown). Alternatively, the first
and second radio technology RF chains 302, 304 may combine receive
and transmit circuitry (e.g., as transceivers associated with SIM-1
and SIM-2 in FIG. 2).
[0054] As described, interference between the first and second
radio technology RF chains 302, 304, such as de-sense and
interpolation, may cause the desired signals to become corrupted
and difficult or impossible to decode. For example, a transmission
signal 330 sent by the first radio technology RF chain 302 may be
errantly received by the second radio technology RF chain 304. In
addition, electronic noise 332 from circuitry, such as the baseband
processor 216, may also contribute to interference on the first and
second radio technology RF chains 302, 304. To avoid such
interference, the multi-technology communication device may
implement various embodiment algorithms to estimate a nonlinear
interference caused by the transmissions signal 330 and cancel the
estimated nonlinear interference from victim signals received by
the second radio technology RF chain 304.
[0055] For the purpose of providing a clear disclosure, signals
received by a wireless communications device will be referred to as
victim signals. However, victim signals may also be transmission
signals experiencing desense caused by incoming received
signals.
[0056] The various embodiments provide efficient algorithms that
may be implemented in circuitry, in software, and in combinations
of circuitry and software for estimating the nonlinear interference
present in a victim signal without requiring a complete
understanding or rigorous mathematical model of the aggressor
signal or sources of the nonlinear interference. The embodiment
algorithms are premised upon a general mathematical model of the
nonlinear interferences, which for completeness is described below
with reference to equations 1-3. These equations are not directly
solvable, and provide a model for structuring that nonlinear
interference cancellation system according to various embodiments
described below beginning with FIG. 4.
[0057] In this mathematical model, the actual nonlinear
interference signal is modeled as the interference experienced by a
victim signal as a result of one or more aggressor signal(s) z(i).
In this model, the actual nonlinear interference signal L(i) caused
by one or more hypothetical aggressor signal(s) z(i) on a
hypothetical victim signal at a time "i" may be represented by the
function:
L(i)= {square root over (JNR)}J(z(i)) [Eq. 1]
where JNR is a jammer to noise ratio (a value that could be
measured at time i) and J(z(i)) is a Jacobian matrix of all
hypothetical aggressor signals z(i). JNR is a value that can be
calculated based on measurements but is not required in the
embodiment algorithms.
[0058] Similarly, the estimated nonlinear interference signal
{circumflex over (L)}(i) for a time "i" may be expressed as:
{circumflex over (L)}(i)= {square root over (JNR)}{circumflex over
(J)}(z(i)) [Eq. 2]
where JNR is again the jammer to noise ratio and (z(i)) is a
Jacobian matrix of all aggressor signals z(i) (discussed in detail
with reference to FIGS. 4-6A below). The estimated function
{circumflex over (L)}(i) is an estimate of the actual nonlinear
interference signal L(i) as discussed above. This estimated
nonlinear interference signal {circumflex over (L)}(i) may be the
result of manipulation of the aggressor signal z(i) by the block
least squares interference filter according to various embodiments
as described below.
[0059] A victim signal y(i), may be the signal actually received by
the multi-technology wireless communications device and may be
degraded as a result of interference from the one or more aggressor
signals z(i). The victim signal y(i) for the time "i" received by
the multi-technology wireless communications device may be
represented as the function:
y(i)= {square root over (SNR)}x(i)+ {square root over
(JNR)}J(z(i))+v(i) [Eq. 3]
where elements of the victim signal y(i) may be expressed in terms
of the signal-to-noise ratio (SNR), the intended receive signal
represented as a function x(i), the jammer-to-noise ratio (JNR) of
equation 2, the Jacobian matrix of all aggressor signals z(i), and
a noise in the victim signal, such as thermal noise an inter-device
interference, represented by the function v(i). As with equations 1
and 2 above, the victim signal in equation 3 is provided as a
mathematical representation illustrating the relationship between
the various signals.
[0060] Theoretically, the intended received signal x(i) may be
obtained by rearranging the terms in Equation 3 to solve for x(i).
A direct solution of these model equations may not be feasible in
real time, particularly within mobile communication devices that
have limited processing power. Therefore, the various embodiments
employ a block least squares interference filter to generate an
estimate of the nonlinear interference signal L(i) without directly
solving equation 1-3.
[0061] FIG. 4 illustrates a nonlinear interference cancellation
system including a block least squares interference filter 400 that
may be used to remove an estimate of the nonlinear interference
from a victim signal in accordance with various embodiments. With
reference to FIGS. 1-4, the block least squares interference filter
400 may be implemented in a multi-technology wireless
communications device (e.g., 110, 120, 200 in FIGS. 1 and 2) in
software, general processing hardware, dedicated hardware, or a
combination of any of the preceding. The block least squares
interference filter 400 may be configured to receive an aggressor
signal 402 and a victim signal 412 at a time "i". The block least
squares interference filter 400 may be configured to produce an
estimated nonlinear interference signal 410 for the time "i".
[0062] In various embodiments, The block least squares interference
filter 400 may be configured to receive an aggressor kernel matrix
406 generated by a kernel matrix generator 404. The aggressor
kernel matrix 406 may be the result of kernel functions of
different order, applied to all or a portion of the aggressor
signal 402. The block least squares interference filter 400 may be
configured to utilize the aggressor kernel matrix to calculate an
estimated nonlinear interference signal 410 for the time "i".
[0063] In some embodiments, the block least squares interference
filter 400 may be a block least squares based machine learning
technique and linear filtering technique implemented in a
multi-technology wireless communications device. For any time "i",
the block least squares interference filter 400 may be implemented
to help identify an intended receive signal x(i) (i.e. the desired
signal 414), the signal the communications device would have
received but for experienced interference, from among the elements
of the actually received victim signal 412 y(i). Given an aggressor
signal 402 z(i), the block least squares interference filter 400
may implement block least squares machine learning algorithms
combined with linear filtering to produce an estimated nonlinear
interference signal 410 that may be cancelled from the victim
signal 412.
[0064] The estimated nonlinear interference signal 410 for the time
"i" may be used by a linear combination function 438 to cancel the
estimated nonlinear interference signal 410 from the victim signal
412. For example, the linear combination function 438 may subtract,
add, or otherwise mathematically manipulate portions of the
estimated nonlinear interference signal 410 affecting the victim
signal 412. Thus, unnecessary elements of the victim signal 412
caused by aggressor signal 402 interference may be removed from the
victim signal 402 and elements obscured by aggressor signal 402
interference may be recaptured. The result of the linear
combination function 438 may be the victim signal with the
nonlinear interference cancelled 412. A demodulator 440 may receive
the victim signal with the nonlinear interference cancelled 412 and
demodulate it to produce the desired signal 414.
[0065] In various embodiments, the block least squares interference
filter 400 may include computer implementations of block least
squares machine learning algorithms. One or more aggressor signals
402 z(i) may be provided as input to the block least squares
interference filter 400 as will be discussed in greater detail with
reference to FIGS. 5-6A below. The aggressor input(s) may be
manipulated by the block least squares interference filter 400 in a
series of mathematical operations and estimations to generate the
estimated nonlinear interference signal 410. Because of the
mathematical complexity associated with calculation of an actual
nonlinear interference signal, machine learning algorithms and
linear filter functions (e.g., Hammerstein structure) may be
implemented to produce an estimate of an experienced nonlinear
interference signal such as the estimated nonlinear interference
signal 410. As such, the various formulae described herein are
mathematical representations of actual and estimated signals that
are utilized or produced by the block least squares interference
filter 400. These mathematical representations may not be actively
calculated by the block least squares interference filter 400, but
are provided to enable one of ordinary skill in the art to realize
the relationships between elements of the various signals as they
are manipulated by the operations described herein.
[0066] As discussed with reference to equations 1 and 2 above, the
estimated nonlinear interference signal 410 may be described in
terms of one or more aggressor signals 402 z(i). Thus, the
production of the estimated nonlinear interference signal 410 may
depend on the manipulation of the aggressor signals 402 by the
block least squares interference filter 400. In some embodiments,
the block least squares interference filter may accept the result
of a kernel function executed on the aggressor signal 402 (i.e.,
aggressor kernel(s)) and may produce multiple aggressor kernel
matrices for use in estimation of nonlinear interference. These
embodiments will be discussed in greater detail with reference to
FIGS. 5-6A below.
[0067] Generating the estimated nonlinear interference signal 410
for the time "i" may be accomplished by the block least squares
interference filter 400 in a semi-blind and universal manner. In
other words, the block least squares interference filter400 may
calculate the estimated nonlinear interference signal 410 knowing
some information about the radio access technology used by the
multi-technology wireless communications device and/or the kind of
interference occurring on the victim signal 412. This information
may include the radio band of the aggressor and/or victim signal
and other transmission information. In embodiments in which the
aggressor signal 402 is converted into an aggressor kernel, the
order of the kernel function may be dictated by the transmission
information. For example, in various embodiments, aggressor signals
transmitted on a particular radio band may require manipulation
using a kernel function of order "b" to produce an aggressor
kernel.
[0068] The aggressor signal 402 may have a mathematical
representation that is a complex structure with imaginary and real
elements. Thus, the aggressor signal may include a real aggressor
signal component and an imaginary aggressor signal component. The
real aggressor signal component may be represented by
z.sub.Real(i), and the imaginary aggressor signal component may be
represented by z.sub.Imaginary(i). As discussed further with
reference to FIG. 6A, an intermediate layer of the block least
squares interference filter may produce a jammer signal estimate
(e.g. intermediate layer outputs526, 528 in FIG. 5) having a
complex structure. Like the aggressor signal components, the
complex jammer signal estimate may include a real jammer signal
estimate component and an imaginary jammer signal estimate
component. A linear filter, such as a finite impulse response
filter, may receive a real jammer signal estimate component and an
imaginary jammer signal estimate component. The linear filter may
use the real and imaginary jammer signal estimate components to
produce a complex estimated nonlinear interference signal (i.e., an
estimated nonlinear interference signal having a complex
structure), as discussed further with reference to FIGS. 5-6A. The
complex estimated nonlinear interference signal may include an
estimated nonlinear interference component (real) and an estimated
nonlinear interference component (imaginary).
[0069] As described, the aggressor signal 402 may be represented as
a function z(i) for the time "i". The kernel generation function
employed by the kernel matrix generator 404 may be one of various
kernel functions such a harmonic or exponential expansion of order
"r", for example z(i). The resulting aggressor kernel may have a
complex structure with both real and imaginary components. Thus,
the aggressor kernel {circumflex over (z)}(i) may be represented
as:
{circumflex over (z)}.sub.Real(i)=Real part of ker(z(i)) [Eq.
4a]
{circumflex over (z)}.sub.Imaginary(i)=Imaginary part of ker(z(i))
[Eq. 4b]
where ker(z(i)) is the application of a selected kernel function on
the aggressor signal 402 z(i) by the kernel matrix generator 404.
The kernel matrix generator 404 may utilize the generated real and
imaginary aggressor kernels to build an aggressor kernel matrix as
will be discussed in greater detail with reference FIGS. 5-6A
below.
[0070] The aggressor kernel matrix 406 may be divided into two
component matrices, a real kernel matrix and an imaginary kernel
matrix. A number of elements in each of the component matrices may
be determined by the order of kernel function. For example, a
kernel function of order=7 may produce four aggressor kernel
components and thus the associated component matrix may have four
elements. Each element may correspond to an execution of a kernel
function of order 1, 3, 5, and 7. The real and imaginary aggressor
kernel matrices may be combined into a combined aggressor kernel
matrix, which may be represented in terms of its components by the
functions:
A.sub.Real(i)=[{circumflex over (z)}.sub.Re,1(i){circumflex over
(z)}.sub.Re,2(i) . . . {circumflex over
(z)}.sub.Re,K(i)].sub.N.times.K [Eq. 5a]
A.sub.Imaginary(i)=[{circumflex over (z)}.sub.Im,1(i){circumflex
over (z)}.sub.Im,2(i) . . . {circumflex over
(z)}.sub.Im,K(i)].sub.N.times.K [Eq. 5b]
A.sub.s(i)=[A.sub.Real(i)A.sub.Imaginary(i)].sub.N.times.2K [Eq.
5c]
where A.sub.Real(i) is the real component of the combined aggressor
kernel matrix, i.e. real aggressor kernel matrix, N and M are a
number of signal samples, K is a number of aggressor kernels,
A.sub.Imaginary(i) is the imaginary component of the combined
aggressor kernel matrix, i.e. imaginary aggressor kernel matrix,
and A.sub.s(i) is a combined aggressor kernel matrix.
[0071] As discussed with reference to FIG. 4 and with further
reference to FIG. 5, the real aggressor kernel matrix, the
imaginary aggressor kernel matrix, and the combined aggressor
kernel matrix may be received at the intermediate layer and used to
produce the real jammer signal estimate component and an imaginary
jammer signal estimate component. The linear filter 530 may receive
the real jammer signal estimate component and an imaginary jammer
signal estimate component and produce the estimated real nonlinear
interference component and the estimated imaginary nonlinear
interference component.
[0072] FIG. 5 illustrates a block least squares interference filter
500 (similar to 400 in FIG. 4) in accordance with various
embodiments. The block least squares interference filter 500 may
include an intermediate layer and an output layer, which may
further the calculation of the estimated nonlinear interference
signal for time "i". The intermediate layer may include one or more
weighting components and linear combinations, collectively
referenced as an intermediate combination 520. The operations of
the intermediate layer may produce intermediate layer output
signals 526, 528 (e.g., a jammer signal estimate), which may be
passed to a linear filter function 530 of the output layer
[0073] In various embodiments, the intermediate layer may include
weighting components that augment the elements of the real and
imaginary aggressor kernel matrices 506, 508 with a set of one or
more weight factors "w". The one or more weight factors may be
defined by a least squares function operating on the combined
aggressor kernel matrix 504. An exemplary least squares function
may be expressed by the function:
{circumflex over
(w)}=(A.sub.S.sup.T(i)A.sub.S(i)).sup.-1A.sub.S.sup.T(i)y(i)[ Eq.
6a]
where {circumflex over (w)}=arg
min.sub.w|y(i)-A.sub.S(i)w.parallel..sup.2 [Eq. 6b]
where {circumflex over (.omega.)} is a matrix of weight factors,
A.sub.s(i) is the combined aggressor kernel matrix 504, and y(i) is
the victim signal 412.
[0074] In some embodiments, a real aggressor kernel matrix 506 and
an imaginary aggressor kernel matrix 508 may be generated based on
the aggressor signal for a time "i". The aggressor kernel matrices
506, 508 may be passed to the intermediate layer from the input
layer. A combined aggressor kernel matrix 504 may be composed of
both the real aggressor kernel matrix 506 and the imaginary
aggressor kernel matrix 508. The kernel matrices 506, 508 may be
passed to an intermediate layer for augmentation with one or more
weight factors and linear combination. In some embodiments, the
output of the intermediate combination 520 may be an intermediate
layer output (real) 526 and an intermediate layer output
(imaginary) 528, which may be passed to a linear filter function
530 of the output layer.
[0075] The intermediate combination 520 may receive the kernel
matrix parts 506, 508, and augment the kernel matrix components
with weight factors. Elements of the augmented aggressor kernel
matrices may be summed by a linear combination during or after the
augmentation. The augmentation and summation may produce an
intermediate layer output (real) 526 and an intermediate layer
output (imaginary) 528. An intermediate layer output s(i) (e.g., a
jammer signal estimate) may be expressed in terms of the weight
factors w and the combined aggressor kernel matrix 504 A.sub.s(i)
by the functions:
{circumflex over (s)}(i)=A.sub.S(i){circumflex over (w)} [Eq.
7]
where s(i) is a vector comprising elements that represent
individual intermediate layer outputs for a time "I", A.sub.s(i) is
a combined aggressor matrix 504, and w are one or more weight
factors. These intermediate layer output elements may be combined
with previous intermediate layer outputs to produce intermediate
layer outputs 526, 528 that may produce a more accurate estimation
of the nonlinear interference. The real and imaginary results of
the augmentation and linear combination are contained in the
intermediate layer output 526, 528 S.sub.Re(i), S.sub.Im (i), which
may be matrices. Intermediate layer output matrices may be
represented in terms of the augmentation and linear combination
results for time "i" by the functions:
S(i).sub.N.times.L=[{circumflex over (s)}(i){circumflex over
(s)}(i-1) . . . {circumflex over (s)}(i+L-1)] [Eq. 8a]
S.sub.S(i)=[S.sub.Re(i)S.sub.Im(i)] [Eq. 8b]
where S(i).sub.N.times.L is a matrix containing intermediate layer
output vectors from a time "I" (current) to a time "i+L-1" for a
sample size "N", and S.sub.S (i) is a general representation of the
intermediate layer outputs (i.e. a combined intermediate layer
output matrix) comprising both hidden layer outputs 526, 528
S.sub.Re (i), S.sub.Im(i).
[0076] The intermediate layer outputs 526, 528 may be passed to a
linear filter function 530 of the output layer. The linear filter
function 530 may have a Hammerstein structure or other finite
impulse response filter function. In various embodiments, the
intermediate layer outputs 526, 528 may be summed and augmented by
a second set of one or more weight factors during execution of the
linear filter function 530 to produce an estimated nonlinear
interference signal 410. The weight factors may be determined using
a least squares method on the intermediate layer outputs 526, 528.
The second set of one or more weights "u" may be described in terms
of the intermediate layer outputs 526, 528 as the function:
{circumflex over
(u)}=(S.sub.S.sup.T(i)S.sub.S(i)).sup.-1S.sub.S.sup.T(i)y(i) [Eq.
9a]
where {circumflex over (u)}=arg
min.sub.u.parallel.y(i)-S.sub.S(i)u.parallel..sup.2 [Eq. 9b]
where u is a matrix of weight factors, S.sub.s(i) is the combined
intermediate layer output matrix (i.e. 526 and 528 combined), and
y(i) is the victim signal 412.
[0077] Results of the linear filter function 530 may be an
estimated nonlinear interference signal 410. The estimated
nonlinear interference signal 410{circumflex over (L)}(i) for the
time "i" may be represented by the function:
{circumflex over (L)}(i)=S.sub.s(i){circumflex over (u)} [Eq.
10]
where {circumflex over (L)}(i) is an estimated nonlinear
interference signal 410, S.sub.s(i) is a combined intermediate
layer outputs, and u is a matrix of a second set of one or more
weight factors. Thus, the estimated nonlinear interference signal
410 is dependent on both the real and imaginary intermediate layer
outputs 526, 528. The produced estimated non-linear interference
may be cancelled from the received victim to obtain an intended
receive signal "x(I)".
[0078] FIG. 6A illustrates interactions between components of the
block least squares interference filter (e.g., 400 in FIG. 4 or 500
in FIG. 5) with an aggressor signal input (e.g., 402 in FIG. 4) in
accordance with various embodiments. With reference to FIGS. 1-6A,
the block least squares interference filter may include an input
layer 600, an intermediate layer 620, and/or an output layer 630.
For purposes of clarity, the block least squares interference
filter is described with reference to a single aggressor signal;
however, multiple aggressor signals may interfere with the victim
signal and consequently multiple aggressor signals may be used to
produce the estimated nonlinear interference signal.
[0079] The input layer 600 may receive the aggressor signal 402 and
may pass it to a kernel matrix generator 604a-b. In some
embodiments, the aggressor signal 402 may be divided into a real
aggressor signal component 502a and an imaginary aggressor signal
component 502b before being passed to the kernel matrix generators
604a, 604b. The kernel matrix generator 604a-b may apply a kernel
function to the signal components 502a-b (i.e., the real and
imaginary components of aggressor signal 402) to produce a real
kernel component 506a-b and an imaginary kernel component 508 a-b
for each execution of the kernel function. Each execution of the
kernel function may use a kernel function of progressively lower
order until a function of a minimum order has been executed. The
real kernel components and the imaginary kernel components may be
added to matrix structures to produce a real aggressor kernel
matrix 506a-b and imaginary aggressor kernel matrix 508a-b. The
real aggressor kernel matrix 506a-b and imaginary aggressor kernel
matrix 508a-b components may also be combined by a Kernel matrix
combiner 606 to generate a combined aggressor kernel matrix
504a-b.
[0080] Each of the real aggressor kernel matrix 506a-b and
imaginary aggressor kernel matrix 508a-b may be passed to an
intermediate layer 620. The real aggressor kernel matrix 506a-b may
be passed to weighting component 622a and intermediate layer linear
combination component 624a of the intermediate layer 620.
Similarly, the imaginary aggressor kernel matrix 508a-b may be
passed to weighting component 622b and intermediate layer linear
combination component 624b.
[0081] In various embodiments, the combined aggressor kernel matrix
504a-b may also be passed to the intermediate layer 620 along with
the real aggressor kernel matrix 506a-b and imaginary aggressor
kernel matrix 508a-b. A least squares function may be executed on
the combined aggressor kernel matrix to produce a set of one or
weight factors. In some examples, these weight factors may be used
by weighting components 622a-b to augment the real and imaginary
aggressor kernel matrices 506a-b, 508a-b.
[0082] At the intermediate layer 620, the weighting components
622a, 622b may augment the real aggressor kernel matrix 506a-b and
imaginary aggressor kernel matrix 508a-b with one or more weight
factors to produce augmented aggressor kernel matrices. In various
embodiments, weighting component 622a may augment the real
aggressor kernel matrix 506a-b. Similarly, the weighting component
622b may augment the imaginary aggressor kernel matrix 508a-b. The
weighting components 622a-b may be individual components of the
block least squares function interference filter implemented with
general purpose hardware, dedicated hardware, and/software.
Alternatively, the weighting components 622a-b may be incorporated
into the intermediate layer linear combination components 624a-b.
Thus, the one or more weight factors may be applied prior to,
during, or after summation of the real and imaginary aggressor
kernel matrices 506a-b, 508a-b by the intermediate layer linear
combination components 624a-b. The augmentation and linear
combination of the aggressor kernel matrices 506, 508 may produce
an intermediate layer output (real) 526 and an intermediate layer
output (imaginary) 528, which may be passed to an output layer
630.
[0083] FIG. 6B illustrates interactions between components of an
output layer 630 including for example a linear filter 530, such as
a finite impulse response filter, in accordance with various
embodiments. With reference to FIGS. 1-6B, the output layer 630
includes a linear filter 530 that may be executed to filter the
real and intermediate layer outputs 526, 528. A result of the
filtering may be the production of an estimated nonlinear
interference 410.
[0084] Each of the intermediate layer outputs 526 and 528 may be
passed to the output layer 630 including a linear filter function
530. The linear filter function 530 may be executed to filter the
intermediate layer outputs 526, 528 and produce an estimated
nonlinear interference 410. In some embodiments the linear filter
function 530 may be a finite impulse response filter having a delay
line 632a(1)-(M) for the intermediate layer output (real) 526, and
a delay line 632b(1)-(M) for the intermediate layer output
(imaginary) 528. A second set of weighting components 633a-d,
635a-d may augment the intermediate layer outputs 526, 528 with one
or more weight factors at each operation of the delay lines
632a(1)-(M), 632b(1)-(M). In some embodiments, the weighting
component 633a-d may augment the intermediate layer output (real)
526 and the weighting component 635a-d may augment the intermediate
layer output (imaginary) 528. The one or more weight factors of the
second set of weighting components 633a-d, 635a-d may be generated
in a manner similar to that of the first weight factors. In an
embodiment, the intermediate layer outputs 526, 528 may be combined
and passed to the output layer 630. A least squares function may be
executed on the combined intermediate layer outputs to produce a
second set of weight factors. The second set of weight factors may
be associated with weighting components 633a-d, 635a-d and used to
augment the real and intermediate components of the intermediate
layer outputs 526, 528, respectively.
[0085] The output layer 630 including the linear filter function
530 may further include a delay line linear combination component
634 for combining the real and imaginary intermediate layer outputs
526, 528. The intermediate layer outputs 526, 528 by be combined
(e.g. summed or multiplied) as they are processed by the respective
delay lines 632a(1)-(M), 632b(1)-(M) and augmented with one or more
weight factors. Each operation of the delay lines and their
associated weighting component are referred to as a "tap" of the
linear filter function. The linear filter function may have "M"
taps and thus may have M-1 weighting components (i.e., d=M+1). A
result of the delay line sampling, second weigh augmentation, and
linear combination may be an estimated nonlinear interference
signal 410.
[0086] In some embodiments, the estimated nonlinear interference
signal 410 may be cancelled or subtracted from the victim signal
412 so that the victim signal 412 may be decoded and understood by
the multi-technology communications device. In some embodiments,
the estimated nonlinear interference signal 410 may be used to
train the weight factors of the block least squares interference
filter.
[0087] In some embodiments, the error of the estimated nonlinear
interference signal 410 may be compared to an error threshold to
determine whether the error is acceptable. Determining that the
error present in an estimation of the nonlinear interference signal
is unacceptable may prompt the block least squares interference
filter 400 to train or retrain the weight factors to reduce the
error in the estimated nonlinear interference signal 410. The
weight factors may be trained using a variety of optimization
algorithms, for example least squares, gradient decent, the
Gauss-Newton algorithm, and the Levenberg-Marquardt algorithm.
Training of the weight factors may be regressively executed to
further reduce the error of the estimated nonlinear interference
signal 410. In some embodiments, satisfactory weight factors may be
reused for subsequent nonlinear interference estimations. The reuse
of previously determined weight factors may be based on one or more
parameters, such as time since the last adjustment of the weight
factors and how the error in the estimated nonlinear interference
signal 410 compares to the error threshold, and the like.
[0088] In the various examples, components of the block least
squares interference filter are shown individually or in
combination. It should be understood that these examples are not
limiting and the various other configurations of the components are
considered. For example, the intermediate layer components are
illustrated as separate components. However, any of the nodes
and/or components may be embodied in combination with other
components, and multiples of the same component may be embodied in
a single component.
[0089] FIG. 7 illustrates a method 700 for canceling nonlinear
interference from a received signal using a block least squares
interference filter (e.g., 400 in FIG. 4 or 500 in FIG. 5) in a
multi-technology wireless communications device in accordance with
various embodiments. With reference to FIGS. 1-7, the method 700
may be executed in a computing device (e.g., 110, 120, 200) using
software, general purpose or dedicated hardware, or a combination
of software and hardware, such as the general purpose processor
206, baseband processor 216, or the like. In block 702, the
multi-technology communication device may receive an aggressor
signal. The aggressor signal may be received by a first radio
access technology of the multi-technology communication device from
a transmission of a second radio access technology of the same
multi-technology communication device.
[0090] In block 704, the multi-technology communication device may
receive a victim signal. The victim signal may be received by the
first radio access technology of the multi-technology communication
device from a transmitting source device separate from the
multi-technology communication device. The victim signal may
initially be unaffected by interference when transmitted from the
transmitting source device. However, the victim signal may
experience interference caused by the aggressor signal during
transmission to the multi-technology communication device.
[0091] In block 706, the multi-technology communication device may
generate a dominant aggressor kernel matrix from the aggressor
signal. The aggressor kernel may include a real component and an
imaginary component. The aggressor signal received by the first
radio access technology of the multi-technology communication
device may be separated into a real component and an imaginary
component. These components may be passed as inputs to a kernel
function such as a harmonic or exponential function (e.g., a
harmonic expansion), where the order of the kernel function may be
dictated by information known about the transmission technology of
the aggressor or victim signal. The kernel function may be executed
(r/2)+1 times where "r" is the highest order of the kernel
function, and for each execution of the kernel function from order
1, 3, 5 . . . "r" the result may be added to an aggressor kernel
matrix. The kernel matrix generator may produce a real aggressor
kernel matrix, an imaginary aggressor kernel matrix and a combined
aggressor kernel matrix including both the real aggressor kernel
matrix and the imaginary aggressor kernel matrix.
[0092] In block 708, the multi-technology communication device may
estimate the nonlinear interference of the victim signal caused by
the aggressor signal(s). This estimation of the nonlinear
interference is discussed in further detail (e.g., with reference
to FIGS. 8 and 9). In block 710, the multi-technology communication
device may cancel an estimated nonlinear interference signal from
the victim signal. Canceling or removing the estimated nonlinear
interference from the victim signal may be implemented in a variety
of known ways, such as filtration, transformation, extraction,
reconstruction, and suppression. In block 712, the multi-technology
communication device may decode the victim signal without presence
of the interference by the aggressor signal(s). In block 714, the
multi-technology communication device may advance to the next time
interval "i" (e.g., move to the current time interval) and begin
the process again with regard to aggressor and victim signals for
the current time "i".
[0093] FIG. 8 illustrates a method 800 for estimating nonlinear
interference using a block least squares interference filter (e.g.,
400 in FIG. 4 or 500 in FIG. 5) in a multi-technology wireless
communications device in accordance with various embodiments. In
one example, the method may be performed by a processor of the
multi-technology communication device. With reference to FIGS. 1-8,
the method 800 may be executed in a computing device (e.g., 110,
120, 200) using software, general purpose or dedicated hardware, or
a combination of software and hardware, such as the general purpose
processor 206, baseband processor 216, or the like. The method 800
may be included in method 700 in FIG. 7 as part of block 708. As
described above, the victim signal and the aggressor signal or
aggressor kernel may be used by the multi-technology communication
device as input signals for the block least squares interference
filter. The victim signal and the aggressor signal or aggressor
kernel may be received by the input layer of the least squares
function interference filter. The real and imaginary aggressor
kernel matrices and a combined aggressor kernel matrix, generated
from the aggressor signal or aggressor kernel may be used as
intermediate layer input signals and maybe manipulated in the
estimation of the estimated nonlinear interference.
[0094] 1-8, in block 802 the multi-technology communication device
may execute a least squares function on the combined aggressor
kernel matrix to produce a first set of weight factors. The least
squares function may be executed on all or a part of the combined
aggressor kernel matrix, and may produce a single set of weights,
or a first set of weights (real) and a first set of weights
(imaginary).
[0095] In block 804, the multi-technology communication device may
augment the aggressor kernel matrices with a first set of weight
factors. As described above, in various embodiments, the weight
factors may be generated using the combined aggressor kernel
matrix, and may, be preprogrammed, and/or trained as described with
reference to FIG. 9.
[0096] In block 806, the multi-technology communication device may
execute a linear combination on both the augmented real aggressor
kernel matrix and the augmented imaginary aggressor kernel matrix
separately. In some embodiments, the augmentation and linear
combination may be executed through mathematical and/or logical
operations. The operations implementing the augmentation may result
in a multiplication of a respective weight factor with a respective
aggressor kernel matrix element. The operations implementing the
linear combination may result in the summation of the augmented
aggressor kernel matrices. In some embodiments, multiplication of
weight factors with the aggressor kernel matrices may occur during
the summation such that a weight factor is multiplied by an
aggressor kernel matrix at subsequent iterations of the summation.
The linear combination of the augmented aggressor kernel matrices
may produce the intermediate layer outputs.
[0097] In determination block 808, the multi-technology
communication device may execute a least squares function on a
combined intermediate layer output matrix to produce a second set
of weight factors. The least squares function may produce a single
set of weight factors, or a second set of weight factors (real) and
a second set of weight factors (imaginary).
[0098] In determination block 810, the multi-technology
communication device may execute a linear filter function at an
output layer. The linear filter function may be an impulse response
filter such as a linear finite impulse response filter, which may
sample and augment and combine the intermediate layer outputs. The
linear filter function may have a delay line for each of the
intermediate layer output real and imaginary. The linear filter
function may augment the intermediate layer outputs with a second
set of weight factors. The augmented intermediate layer outputs may
be linearly combined at each set of the delay lines to produce a
single estimated linear interference signal.
[0099] FIG. 9 illustrates a method 900 for training weight factors
for use in a block least squares interference filter (e.g., 400 in
FIG. 4 or 500 in FIG. 5) in a multi-technology wireless
communications device in accordance with various embodiments. With
reference to FIGS. 1-9, the method 900 may be executed in a
computing device (e.g., 110, 120, 200) using software, general
purpose or dedicated hardware, or a combination of software and
hardware, such as the general purpose processor 206, baseband
processor 216, or the like. In block 902, the multi-technology
communication device may select the weight factors for augmenting
the aggressor kernel matrices and the second set of weight factors
for use in the linear filter function. As described, in various
embodiments, the weight factors may be determined at random, be
preprogrammed, and/or trained. In some embodiments the weight
factors may be trained using a series of mathematical operations in
which the first weight factors are determined using the combined
aggressor kernel matrix, and the second set of weight factors is
trained using the intermediate layer outputs and a second set of
mathematical operations.
[0100] In block 904, the multi-technology communication device may
determine an error present in the estimate of the nonlinear
interference. Various known methods for determining the error of a
function may be used to determine the error in block 904. In some
embodiments, the error calculation may be for the mean square error
of the estimated nonlinear interference compared with the nonlinear
interference signal caused by the aggressor signal(s).
[0101] In determination block 906, the multi-technology
communication device may determine whether the estimation of the
nonlinear interference is complete. Estimation of the nonlinear
interference may be considered to be complete at such time as the
block least squares interference filter has finished execution and
an estimated nonlinear interference signal has been obtained (i.e.,
the real and imaginary estimated nonlinear interference have been
combined). In response to determining that the estimation of the
nonlinear interference is incomplete (i.e., determination block
906="No"), the multi-technology communication device may train the
weight factors in block 908. In various embodiments, the weight
factors may be trained using a variety of optimization algorithms,
for example least squares, gradient decent, the Gauss-Newton
algorithm, and the Levenberg-Marquardt algorithm. Training of the
weight factors may be regressively executed to further reduce the
error of the estimated nonlinear interference. The multi-technology
communication device may continue selecting weight factors for
augmenting the aggressor kernel matrices in block 902. Selection
may include the newly trained weight factors.
[0102] In response to determining that the estimation of the
nonlinear interference is complete (i.e., determination block
906="Yes"), the multi-technology communication device may determine
whether the nonlinear interference cancellation exceeds an
efficiency threshold in determination block 910. The determination
of whether the nonlinear interference cancellation exceeds the
efficiency threshold may be a measure of whether the nonlinear
interference is cancelled sufficient to enable the multi-technology
communication device to decode and use the victim signal. The
efficiency threshold may be a precalculated or predetermined value
based on historical observations of a level of accuracy present in
an estimated nonlinear interference signal that is necessary to
enable proper decoding of a victim signal. In some embodiments, the
efficiency threshold may be based on the error value determination
of the nonlinear interference in block 904, in which the error
level may be compared to an acceptable error level. In some
embodiments, the efficiency threshold may be based on a success
rate for decoding and using the victim signal.
[0103] In response to determining that the nonlinear interference
cancellation does not exceed the efficiency threshold (i.e.,
determination block 910="No"), the multi-technology communication
device may continue to train the weight factors in block 908.
Training the weight factors may reduce the amount of error in the
estimated nonlinear interference so that the cancellation of the
estimated nonlinear interference may result in greater success of
decoding and using the victim signal.
[0104] In response to determining that the nonlinear interference
cancellation does exceed the efficiency threshold (i.e.,
determination block 910="Yes"), the multi-technology communication
device may reuse the weight factors for subsequent estimation and
cancellation of nonlinear interference in block 912. As described,
the multi-technology communication device may not always train the
weight factors when estimating the nonlinear interference. The
nonlinear interference caused by the one or more aggressor signals
may vary by different amounts under various conditions. In some
embodiments, the variation in the nonlinear interference may be
small enough that the previously trained weight factors may result
in a sufficiently accurate estimated nonlinear interference that
further training is unnecessary. Determining when to train the
weight factors or reuse the weight factors may be based on one or
more criteria, including time, measurements of the aggressor
signal(s), victim signal quality, and nonlinear interference noise
cancellation efficiency, for example including error of the
estimated nonlinear interference and/or success of decoding and
using the victim signal.
[0105] In some embodiments, the method 900 may be executed at
various times before, during, or after the execution of the method
700 and the method 800. For example, the method 900 may be executed
to calculate at least some of the weight factors before they are
used to augment the aggressor kernel matrices in blocks 804. In
some embodiments, certain blocks of the method 900 the method may
not execute contiguously, but may instead execute interspersed with
the blocks of the methods 700, 800.
[0106] In other words, the methods may manage interference such as
signal interference (e.g., non-linear interference) that is
received in a multi-technology communication device. Managing or
analyzing interference may include filtering a received aggressor
signal using a block least squares filtering construct, or
interference. The block least squares filtering construct may
include a number of layers (input layer, hidden layer, output
layer, etc.) in which different mathematical operations are
executed, thereby extracting a numerical representation of
estimated interference from the received aggressor signal. The
multi-technology communication device may receive an aggressor
signal (i.e., a signal interfering with or impeding another
received signal) at an input layer of the block least squares
filtering construct. The multi-technology communication device may
generate a real aggressor kernel matrix, an imaginary aggressor
kernel matrix, and a combined aggressor kernel matrix including
elements or components (i.e., elements represented by real numbers
and elements represented by imaginary numbers) from both the real
and imaginary aggressor kernel matrices. The multi-technology
communication device may augment the real aggressor kernel matrix
and the imaginary aggressor kernel matrix with weight factors
(weights, weighting components) at an intermediate layer of the
block least squares filtering construct to produce augmented or
combined real and imaginary aggressor kernel matrices. Augmentation
may include multiplying each element of the real and imaginary
aggressor kernel matrices by a corresponding weight element (i.e.,
a multiplier). The multi-technology communication device may also
linearly combine, sum, or add the augmented real aggressor kernel
matrix and the augmented imaginary aggressor kernel matrix at the
intermediate layer to produce real intermediate layer outputs and
imaginary intermediate layer outputs.
[0107] The multi-technology communication device may execute a
linear filter function, FIR filter, finite impulse response filter,
or Hammerstein structure on both the real intermediate layer
outputs and the imaginary intermediate layer outputs at an output
layer of the block least squares filtering construct to obtain
estimated nonlinear interference. A result of the
filtering/extracting may be an estimated interference (estimated
nonlinear interference, estimated interference signal), which may
be subtracted from the received victim signal (i.e., the signal
subject to interference by the aggressor signal) to produce a
mathematical representation of the intended received signal.
[0108] FIG. 10 illustrates an exemplary multi-technology
communication device 1000 suitable for use with the various
embodiments. The multi-technology communication device 1000 may be
similar to the multi-technology device 110, 120, 200 (e.g., FIGS. 1
and 2). With reference to FIGS. 1-10, the multi-technology
communication device 1000 may include a processor 1002 coupled to a
touchscreen controller 1004 and an internal memory 1006. The
processor 1002 may be one or more multicore integrated circuits
designated for general or specific processing tasks. The internal
memory 1006 may be volatile or non-volatile memory, and may also be
secure and/or encrypted memory, or unsecure and/or unencrypted
memory, or any combination thereof. The touchscreen controller 1004
and the processor 1002 may also be coupled to a touchscreen panel
1012, such as a resistive-sensing touchscreen, capacitive-sensing
touchscreen, infrared sensing touchscreen, etc. Additionally, the
display of the multi-technology communication device 1000 need not
have touch screen capability.
[0109] The multi-technology communication device 1000 may have two
or more cellular network transceivers 1008, 1009 coupled to
antennae 1010, 1011, for sending and receiving communications via a
cellular communication network. The combination of the transceiver
1008 or 1009 and the associated antenna 1010 or 1011, and
associated components, is referred to herein as a radio frequency
(RF) chain. The cellular network transceivers 1008, 1009 may be
coupled to the processor 1002, which is configured with
processor-executable instructions to perform operations of the
embodiment methods described above. The cellular network
transceivers 1008, 1009 and antennae 1010, 1011 may be used with
the above-mentioned circuitry to implement the various wireless
transmission protocol stacks and interfaces. The multi-technology
communication device 1000 may include one or more cellular network
wireless modem chips 1016 coupled to the processor and the cellular
network transceivers 1008, 1009 and configured to enable
communication via cellular communication networks.
[0110] The multi-technology communication device 1000 may include a
peripheral device connection interface 1018 coupled to the
processor 1002. The peripheral device connection interface 1018 may
be singularly configured to accept one type of connection, or may
be configured to accept various types of physical and communication
connections, common or proprietary, such as USB, FireWire,
Thunderbolt, or PCIe. The peripheral device connection interface
1018 may also be coupled to a similarly configured peripheral
device connection port (not shown).
[0111] The multi-technology communication device 1000 may also
include speakers 1014 for providing audio outputs. The
multi-technology communication device 1000 may also include a
housing 1020, constructed of a plastic, metal, or a combination of
materials, for containing all or some of the components discussed
herein. The multi-technology communication device 1000 may include
a power source 1022 coupled to the processor 1002, such as a
disposable or rechargeable battery. The rechargeable battery may
also be coupled to the peripheral device connection port to receive
a charging current from a source external to the multi-technology
communication device 1000. The multi-technology communication
device 1000 may also include a physical button 1024 for receiving
user inputs. The multi-technology communication device 1000 may
also include a power button 1026 for turning the multi-technology
communication device 1000 on and off.
[0112] The foregoing method descriptions and the process flow
diagrams are provided merely as illustrative examples and are not
intended to require or imply that the operations of the various
embodiments must be performed in the order presented. As will be
appreciated by one of skill in the art the order of operations in
the foregoing embodiments may be performed in any order. Words such
as "thereafter," "then," "next," etc. are not intended to limit the
order of the operations; these words are simply used to guide the
reader through the description of the methods. Further, any
reference to claim elements in the singular, for example, using the
articles "a," "an" or "the" is not to be construed as limiting the
element to the singular.
[0113] The various illustrative logical blocks, modules, circuits,
and algorithm operations described in connection with the
embodiments disclosed herein may be implemented as electronic
hardware, computer software, or combinations of both. To clearly
illustrate this interchangeability of hardware and software,
various illustrative components, blocks, modules, circuits, and
operations have been described above generally in terms of their
functionality. Whether such functionality is implemented as
hardware or software depends upon the particular application and
design constraints imposed on the overall system. Skilled artisans
may implement the described functionality in varying ways for each
particular application, but such implementation decisions should
not be interpreted as causing a departure from the scope of the
present claims.
[0114] The hardware used to implement the various illustrative
logics, logical blocks, modules, and circuits described in
connection with the various embodiments may be implemented or
performed with a general purpose processor, a digital signal
processor (DSP), an application specific integrated circuit (ASIC),
a field programmable gate array (FPGA) or other programmable logic
device, discrete gate or transistor logic, discrete hardware
components, or any combination thereof designed to perform the
functions described herein. A general-purpose processor may be a
microprocessor, but, in the alternative, the processor may be any
conventional processor, controller, microcontroller, or state
machine A processor may also be implemented as a combination of
computing devices, e.g., a combination of a DSP and a
microprocessor, a plurality of microprocessors, one or more
microprocessors in conjunction with a DSP core, or any other such
configuration. Alternatively, some operations or methods may be
performed by circuitry that is specific to a given function.
[0115] In one or more exemplary embodiments, the functions
described may be implemented in hardware, software, firmware, or
any combination thereof. If implemented in software, the functions
may be stored as one or more instructions or code on a
non-transitory computer-readable storage medium or non-transitory
processor-readable storage medium. The operations of a method or
algorithm disclosed herein may be embodied in a
processor-executable software module, which may reside on a
non-transitory computer-readable or processor-readable storage
medium. Non-transitory computer-readable or processor-readable
storage media may be any storage media that may be accessed by a
computer or a processor. By way of example but not limitation, such
non-transitory computer-readable or processor-readable storage
media may include RAM, ROM, EEPROM, FLASH memory, CD-ROM or other
optical disk storage, magnetic disk storage or other magnetic
storage devices, or any other medium that may be used to store
desired program code in the form of instructions or data structures
and that may be accessed by a computer. Disk and disc, as used
herein, includes compact disc (CD), laser disc, optical disc,
digital versatile disc (DVD), floppy disk, and Blu-ray disc where
disks usually reproduce data magnetically, while discs reproduce
data optically with lasers. Combinations of the above are also
included within the scope of non-transitory computer-readable and
processor-readable media. Additionally, the operations of a method
or algorithm may reside as one or any combination or set of codes
and/or instructions on a non-transitory processor-readable storage
medium and/or computer-readable storage medium, which may be
incorporated into a computer program product.
[0116] The preceding description of the disclosed embodiments is
provided to enable any person skilled in the art to make or use the
present claims. Various modifications to these embodiments will be
readily apparent to those skilled in the art, and the generic
principles defined herein may be applied to other embodiments
without departing from the scope of the claims. Thus, the present
invention is not intended to be limited to the embodiments shown
herein but is to be accorded the widest scope consistent with the
following claims and the principles and novel features disclosed
herein.
* * * * *